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Optimal Hiring and Retention Policies for Heterogeneous Workers Who Learn

Author

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  • Alessandro Arlotto

    (The Fuqua School of Business, Duke University, Durham, North Carolina 27708)

  • Stephen E. Chick

    (Technology and Operations Management Area, INSEAD, 77305 Fontainebleau France)

  • Noah Gans

    (Operations and Information Management Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

We study the hiring and retention of heterogeneous workers who learn over time. We show that the problem can be analyzed as an infinite-armed bandit with switching costs, and we apply results from Bergemann and Välimäki [Bergemann D, Välimäki J (2001) Stationary multi-choice bandit problems. J. Econom. Dynam. Control 25(10):1585--1594] to characterize the optimal hiring and retention policy. For problems with Gaussian data, we develop approximations that allow the efficient implementation of the optimal policy and the evaluation of its performance. Our numerical examples demonstrate that the value of active monitoring and screening of employees can be substantial. This paper was accepted by Yossi Aviv, operations management.

Suggested Citation

  • Alessandro Arlotto & Stephen E. Chick & Noah Gans, 2014. "Optimal Hiring and Retention Policies for Heterogeneous Workers Who Learn," Management Science, INFORMS, vol. 60(1), pages 110-129, January.
    • Handle: RePEc:inm:ormnsc:v:60:y:2014:i:1:p:110-129
    DOI: 10.1287/mnsc.2013.1754
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    References listed on IDEAS

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    9. Goldman, Matt & Rao, Justin M., 2017. "Optimal stopping in the NBA: Sequential search and the shot clock," Journal of Economic Behavior & Organization, Elsevier, vol. 136(C), pages 107-124.
    10. Joshua Becker & Douglas Guilbeault & Ned Smith, 2021. "The Crowd Classification Problem: Social Dynamics of Binary Choice Accuracy," Papers 2104.11300, arXiv.org.
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